Evidence map›Paper›PMID 36810591›Full record

ArticleLaboratory medicine2023

3D Printing as an Effective Quality Assurance Implementation in Massive-Scale SARS-CoV-2 Testing at a SwabSeq Next-Generation Sequencing Laboratory.

Laila M Sathe, Nishrat N Khan, Jazmine M Williams, Rosita Saul, Kane Jajieh, Maryam R Sartippour, Rachel Young, Joanna Xie, Dawn M Marquette, Tiffany Duncan and 2 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Laboratory medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 97% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Laila M SatheDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Nishrat N KhanDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Jazmine M WilliamsDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Rosita SaulDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Kane JajiehDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Maryam R SartippourDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Rachel YoungDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Joanna XieUCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Dawn M MarquetteDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Tiffany DuncanUCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Eleazar EskinDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.
Valerie A ArboledaDepartment of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US.ORCID 0000-0002-9687-9122
University of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Massive-scale SARS-CoV-2 testing using the SwabSeq diagnostic platform came with quality assurance challenges due to the novelty and scale of sequencing-based testing. The SwabSeq platform relies on accurate mapping between specimen identifiers and molecular barcodes to match a result back to a patient specimen. To identify and mitigate mapping errors, we instituted quality control using placement of negative controls within a rack of patient samples. We designed 2-dimensional paper templates to fit over a 96-position rack of specimens with holes to show the control tube placements. We designed and 3-dimensionally printed plastic templates that fit onto 4 racks of patient specimens and provide accurate indications of the correct control tube placements. The final plastic templates dramatically reduced plate mapping errors from 22.55% in January 2021 to less than 1% after implementation and training in January 2021. We show how 3D printing can be a cost-effective quality assurance tool to mitigate human error in the clinical laboratory.

Indexed as

COVID-19SARS-CoV-2COVID-19 TestingHigh-Throughput Nucleotide SequencingHumansPlasticsPrinting, Three-DimensionalPlastics3D printinggeneticsquality assuranceSARS-CoV-2sequencingvirology

Identifiers

PMID36810591
PMCPMC13396139
OpenAlexW4321494686

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.